def answer(test_path):
import warnings
warnings.filterwarnings("ignore")
import time
t0 = time.time()
from learning import process_test_data, training_data, training_answers
from sklearn.cluster.k_means_ import KMeans
from sklearn.linear_model.logistic import LogisticRegression
test_data = process_test_data(test_path)
km = KMeans()
km.fit(training_data, training_answers)
myNum = km.predict(test_data).item()
numX = [1, 2, 4, 2, 7, 0, 2, 7, 4, 3, 2, 1, 4, 5, 5, 1, 3, 0, 4, 2]
numbers = [[num] for num in numX]
letX = [
'a', 'a', 'o', 'a', 'o', 'o', 'a', 'a', 'o', 'a', 'a', 'o', 'a', 'o',
'o', 'o', 'a', 'a', 'o', 'a'
]
letters = [[letter] for letter in letX]
lr = LogisticRegression()
lr.fit(numbers, letters)
ans = lr.predict(myNum).item()
t1 = time.time()
return [ans, t1 - t0]
def answer(test_path):
import time
t0 = time.time()
from learning import process_test_data, training_data, training_answers
from sklearn.neural_network import MLPClassifier
test_data = process_test_data(test_path)
mlp = MLPClassifier()
mlp.fit(training_data, training_answers)
ans = mlp.predict(test_data).item()
t1 = time.time()
return [ans, t1 - t0]
def answer(test_path):
import time
t0 = time.time()
from learning import process_test_data, training_data, training_answers
from sklearn.linear_model.logistic import LogisticRegression
test_data = process_test_data(test_path)
lr = LogisticRegression()
lr.fit(training_data, training_answers)
ans = lr.predict(test_data).item()
t1 = time.time()
return [ans, t1 - t0]
def answer(test_path):
import time
t0 = time.time()
from learning import process_test_data, training_data, training_answers
from sklearn.neighbors import KNeighborsClassifier
test_data = process_test_data(test_path)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(training_data, training_answers)
ans = knn.predict(test_data)
t1 = time.time()
return [ans, t1 - t0]
def answer(test_path):
import time
t0 = time.time()
from learning import process_test_data, training_data, training_answers
from sklearn import tree
test_data = process_test_data(test_path)
clf = tree.DecisionTreeClassifier()
clf.fit(training_data, training_answers)
ans = clf.predict(test_data)
t1 = time.time()
return [ans, t1 - t0]